A Novel Human Microbe-Disease Association Prediction Method Based on the Bidirectional Weighted Network

The survival of human beings is inseparable from microbes. More and more studies have proved that microbes can affect human physiological processes in various aspects and are closely related to some human diseases. In this paper, based on known microbe-disease associations, a bidirectional weighted network was constructed by integrating the schemes of normalized Gaussian interactions and bidirectional recommendations firstly. And then, based on the newly constructed bidirectional network, a computational model called BWNMHMDA was developed to predict potential relationships between microbes and diseases. Finally, in order to evaluate the superiority of the new prediction model BWNMHMDA, the framework of LOOCV and 5-fold cross validation were implemented, and simulation results indicated that BWNMHMDA could achieve reliable AUCs of 0.9127 and 0.8967 ± 0.0027 in these two different frameworks respectively, which is outperformed some state-of-the-art methods. Moreover, case studies of asthma, colorectal carcinoma, and chronic obstructive pulmonary disease were implemented to further estimate the performance of BWNMHMDA. Experimental results showed that there are 10, 9, and 8 out of the top 10 predicted microbes having been confirmed by related literature in these three kinds of case studies separately, which also demonstrated that our new model BWNMHMDA could achieve satisfying prediction performance

Neoproterozoic to Paleozoic long-lived accretionary orogeny in the northern Tarim Craton

The Tarim Craton, located in the center of Asia, was involved in the assembly and breakup of the Rodinia supercontinent during the Neoproterozoic and the subduction-accretion of the Central Asian Orogenic Belt (CAOB) during the Paleozoic. However, its tectonic evolution during these events is controversial, and a link between the Neoproterozoic and Paleozoic tectonic processes is missing. Here we present zircon U-Pb ages, Hf isotopes, and whole-rock geochemical data for the extensive granitoids in the western Kuruktag area, northeastern Tarim Craton. Three distinct periods of granitoid magmatism are evident: circa 830–820 Ma, 660–630 Ma, and 420–400 Ma. The magma sources, melting conditions (pressure, temperature, and water availability), and tectonic settings of various granitoids from each period are determined. Based on our results and the geological, geochronological, geochemical, and isotopic data from adjacent areas, a long-lived accretionary orogenic model is proposed. This model involves an early phase (circa 950–780 Ma) of southward advancing accretion from the Tianshan to northern Tarim and a late phase (circa 780–600 Ma) of northward retreating accretion, followed by back-arc opening and subsequent bidirectional subduction (circa 460–400 Ma) of a composite back-arc basin (i.e., the South Tianshan Ocean). Our model highlights a long-lived accretionary history of the southwestern CAOB, which may have initiated as part of the circum-Rodinia subduction zone and was comparable with events occurring at the southern margin of the Siberian Craton, thus challenging the traditional southward migrating accretionary models for the CAOB

Identifying Cancer Driver Pathways Based on the Mouth Brooding Fish Algorithm

Identifying the driver genes of cancer progression is of great significance in improving our understanding of the causes of cancer and promoting the development of personalized treatment. In this paper, we identify the driver genes at the pathway level via an existing intelligent optimization algorithm, named the Mouth Brooding Fish (MBF) algorithm. Many methods based on the maximum weight submatrix model to identify driver pathways attach equal importance to coverage and exclusivity and assign them equal weight, but those methods ignore the impact of mutational heterogeneity. Here, we use principal component analysis (PCA) to incorporate covariate data to reduce the complexity of the algorithm and construct a maximum weight submatrix model considering different weights of coverage and exclusivity. Using this strategy, the unfavorable effect of mutational heterogeneity is overcome to some extent. Data involving lung adenocarcinoma and glioblastoma multiforme were tested with this method and the results compared with the MDPFinder, Dendrix, and Mutex methods. When the driver pathway size was 10, the recognition accuracy of the MBF method reached 80% in both datasets, and the weight values of the submatrix were 1.7 and 1.89, respectively, which are better than those of the compared methods. At the same time, in the signal pathway enrichment analysis, the important role of the driver genes identified by our MBF method in the cancer signaling pathway is revealed, and the validity of these driver genes is demonstrated from the perspective of their biological effects

A Probabilistic Matrix Factorization Method for Identifying lncRNA-Disease Associations

Recently, an increasing number of studies have indicated that long-non-coding RNAs (lncRNAs) can participate in various crucial biological processes and can also be used as the most promising biomarkers for the treatment of certain diseases such as coronary artery disease and various cancers. Due to costs and time complexity, the number of possible disease-related lncRNAs that can be verified by traditional biological experiments is very limited. Therefore, in recent years, it has been very popular to use computational models to predict potential disease-lncRNA associations. In this study, we constructed three kinds of association networks, namely the lncRNA-miRNA association network, the miRNA-disease association network, and the lncRNA-disease correlation network firstly. Then, through integrating these three newly constructed association networks, we constructed an lncRNA-disease weighted association network, which would be further updated by adopting the KNN algorithm based on the semantic similarity of diseases and the similarity of lncRNA functions. Thereafter, according to the updated lncRNA-disease weighted association network, a novel computational model called PMFILDA was proposed to infer potential lncRNA-disease associations based on the probability matrix decomposition. Finally, to evaluate the superiority of the new prediction model PMFILDA, we performed Leave One Out Cross-Validation (LOOCV) based on strongly validated data filtered from MNDR and the simulation results indicated that the performance of PMFILDA was better than some state-of-the-art methods. Moreover, case studies of breast cancer, lung cancer, and colorectal cancer were implemented to further estimate the performance of PMFILDA, and simulation results illustrated that PMFILDA could achieve satisfying prediction performance as well